# Practical: 3 Logistic Regression

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

df = pd.read_csv('pima-diabetes.csv')

x=df.iloc[:,:-1]
y=df.iloc[:,-1]

ss = StandardScaler()
x_scaled = ss.fit_transform(x)

xtrain, xtest, ytrain, ytest = train_test_split(x_scaled, y, test_size=0.25, random_state=1)

lr = LogisticRegression()
lr.fit(xtrain,ytrain)
predictions = lr.predict(xtest)

accuracy_score(ytest,predictions)